The classification of neurons into distinct types is a fundamental endeavor in neuroscience. Neuronal classification allows one to gain insight into the building blocks of the nervous system, is essential for a mechanistic understanding of the function of the nervous system and is a prerequisite for unambiguous communication between investigators. No single unequivocal categorization scheme exists yet for neurons in the mammalian cerebral cortex. The classification based on morphological characteristics has led to tremendous advances in our understanding of the nervous system, yet is often ambiguous in cortical neurons because many morphological properties are difficult to parameterize. Other classifications based on immunohistochemistry or electrophysiology have been helpful but, alone, fail to capture the rich diversity of cortical neurons. Evidence indicates that distinct neuron types express different genes. Thus, in principle, the gene expression pattern could be used to generate an unambiguous and objective classification scheme. Furthermore, a classification based on gene expression would allow one, using molecular approaches, to selectively tag and perturb a given neuron type both for basic research and for clinical purposes. However, classifying neurons exclusively based on their gene expression pattern is, a priori, uninformative with regard to their function, location or integration into the cortical network. The goal of this proposal is to classify cortical neurons based on those genes that best predict neuronal function and location. Thus, to find those genes we need to correlate the transcriptional profile of a neuron in the mammalian cerebral cortex to its function and location. We propose to investigate the primary visual cortex because it is the cortical sensory area where the function of neurons has been described in greatest detail. We will perform calcium imaging of the primary visual cortex of mice to determine the tuning of visual cortical neurons in response to visual stimuli. We will tag the imaged neurons with photoactivatable GFP. We will harvest the RNA from the labeled neurons. We will perform next generation RNAseq to reveal the transcriptional profile of each individual neuron imaged in vivo. We will correlate the transcriptional profile of a neuron with its specific response to visual stimuli. Finally, we will se clustering algorithms and principal component analysis to classify neurons in different types based on those genes that best correlate with function. The result will be a genetically based classification method that provides functional information about cell types.